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The discussion in this podcast comprehensively outlines the NIST Risk Management Framework (RMF), presenting it as the definitive gold standard for cybersecurity governance in the United States federal government. It details the RMF's evolution from a compliance-focused model to a dynamic, risk-based, and continuous process, particularly highlighting its seven sequential steps: Prepare, Categorise, Select, Implement, Assess, Authorise, and Monitor. The document emphasises the RMF's symbiotic relationship with supporting NIST publications like FIPS 199, FIPS 200, and SP 800-53, which provide its foundational principles and comprehensive control catalogue. Furthermore, it distinguishes the RMF from the NIST Cybersecurity Framework (CSF) by their respective scopes and applications, while also exploring the operationalisation of RMF in real-world scenarios such as FedRAMP and its adaptability to emerging technologies like AI. Finally, it addresses common implementation challenges and critiques, concluding that these are often issues of culture and execution rather than fundamental flaws in the framework itself.
 By HelloInfoSec
By HelloInfoSecThe discussion in this podcast comprehensively outlines the NIST Risk Management Framework (RMF), presenting it as the definitive gold standard for cybersecurity governance in the United States federal government. It details the RMF's evolution from a compliance-focused model to a dynamic, risk-based, and continuous process, particularly highlighting its seven sequential steps: Prepare, Categorise, Select, Implement, Assess, Authorise, and Monitor. The document emphasises the RMF's symbiotic relationship with supporting NIST publications like FIPS 199, FIPS 200, and SP 800-53, which provide its foundational principles and comprehensive control catalogue. Furthermore, it distinguishes the RMF from the NIST Cybersecurity Framework (CSF) by their respective scopes and applications, while also exploring the operationalisation of RMF in real-world scenarios such as FedRAMP and its adaptability to emerging technologies like AI. Finally, it addresses common implementation challenges and critiques, concluding that these are often issues of culture and execution rather than fundamental flaws in the framework itself.